The "F1 score" is a combination of two other commonly used metrics in binary classification: precision (P) and recall (R).

The F1 score is the harmonic mean of precision and recall, and the "F" in F1 stands for "F-measure."

The F-measure is a way to balance the trade-off between precision and recall. Precision measures the proportion of true positives among all predicted positives, while recall measures the proportion of true positives among all actual positives. 

These two metrics are often inversely related, meaning that when you increase one, the other may decrease. 

The F1 score provides a single value that takes both precision and recall into account and helps you assess the overall performance of a binary classification model.

The formula for calculating the F1 score is:

F1 Score = 2 * (Precision * Recall) / (Precision + Recall)

The use of the "F" in F1 is to signify that it's a combination of precision and recall. It's a way to represent the balance between these two metrics in a single value. 

The F1 score is particularly useful when you want to find a balance between making accurate positive predictions (precision) and capturing all positive instances (recall) in situations where there might be an imbalance between the classes or when both precision and recall are important.